Identification of cell-specific epigenetic patterns associated with chondroitin sulfate treatment response in an endemic arthritis, Kashin-Beck disease.

Aims: To assess the alterations in cell-specific DNA methylation associated with chondroitin sulphate response using peripheral blood collected from Kashin-Beck disease (KBD) patients before initiation of chondroitin sulphate treatment. Methods: Peripheral blood samples were collected from KBD patients at baseline of chondroitin sulphate treatment. Methylation profiles were generated using reduced representation bisulphite sequencing (RRBS) from peripheral blood. Differentially methylated regions (DMRs) were identified using MethylKit, while DMR-related genes were defined as those annotated to the gene body or 2.2-kilobase upstream regions of DMRs. Selected DMR-related genes were further validated by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) to assess expression levels. Tensor composition analysis was performed to identify cell-specific differential DNA methylation from bulk tissue. Results: This study revealed 21,060 hypermethylated and 44,472 hypomethylated DMRs, and 13,194 hypermethylated and 22,448 hypomethylated CpG islands for differential global methylation for chondroitin sulphate treatment response. A total of 12,666 DMR-related genes containing DMRs were identified in their promoter regions, such as CHL1 (false discovery rate (FDR) = 2.11 × 10-11), RIC8A (FDR = 7.05 × 10-4), and SOX12 (FDR = 1.43 × 10-3). Additionally, RIC8A and CHL1 were hypermethylated in responders, while SOX12 was hypomethylated in responders, all showing decreased gene expression. The patterns of cell-specific differential global methylation associated with chondroitin sulphate response were observed. Specifically, we found that DMRs located in TESPA1 and ATP11A exhibited differential DNA methylation between responders and non-responders in granulocytes, monocytes, and B cells. Conclusion: Our study identified cell-specific changes in DNA methylation associated with chondroitin sulphate response in KBD patients.

Disadvantaged social status contributed to sleep disorders: An observational and genome-wide gene-environment interaction analysis.

BACKGROUND: Sleep is a natural and essential physiological need for individuals. Our study aimed to research the associations between accumulated social risks and sleep disorders. METHODS: In this study, we came up with a polysocial risk score (PsRS), which is a cumulative social risk index composed of 13 social determinants of health. This research includes 239,165 individuals with sleep disorders and social determinants of health data from the UK Biobank cohort. First, logistic regression models were performed to examine the associations of social determinants of health and sleep disorders, including chronotype, narcolepsy, insomnia, snoring, short and long sleep duration. Then, PsRS was calculated based on statistically significant social determinants of health for each sleep disorder. Third, a genome-wide gene-environment interaction study was conducted to explore the interactions between single-nucleotide polymorphisms and PsRS in relation to sleep disorders. RESULTS: Higher PsRS scores were associated with worse sleep status, with the adjusted odds ratio (OR) ranging from 1.10 (95% Confidence interval [CI]: 1.09-1.11) to 1.29 (95% CI: 1.27-1.30) for sleep disorders. Emotional stress (OR = 1.36, 95% CI: 1.28-1.43) and not in paid employment (OR = 2.62, 95% CI: 2.51-2.74) were found to have significant contributions for sleep disorders. Moreover, multiple single-nucleotide polymorphisms were discovered to have interactions with PsRS, such as FRAS1 (P = 2.57 × 10-14) and CACNA1A (P = 8.62 × 10-14) for narcolepsy, and ACKR3 (P = 1.24 × 10-8) for long sleep. CONCLUSIONS: Our findings suggested that cumulative social risks was associated with sleep disorders, while the interactions between genetic susceptibility and disadvantaged social status are risk factors for the development of sleep disorders.

Whole-Transcriptome Sequencing of Knee Joint Cartilage from Kashin-Beck Disease and Osteoarthritis Patients.

The aim of this study was to provide a comprehensive understanding of similarities and differences in mRNAs, lncRNAs, and circRNAs within cartilage for Kashin-Beck disease (KBD) compared to osteoarthritis (OA). We conducted a comparison of the expression profiles of mRNAs, lncRNAs, and circRNAs via whole-transcriptome sequencing in eight KBD and ten OA individuals. To facilitate functional annotation-enriched analysis for differentially expressed (DE) genes, DE lncRNAs, and DE circRNAs, we employed bioinformatic analysis utilizing Gene Ontology (GO) and KEGG. Additionally, using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), we validated the expression levels of four cartilage-related genes in chondrocytes. We identified a total of 43 DE mRNAs, 1451 DE lncRNAs, and 305 DE circRNAs in KBD cartilage tissue compared to OA (q value < 0.05; |log2FC| > 1). We also performed competing endogenous RNA network analysis, which identified a total of 65 lncRNA-mRNA interactions and 4714 miRNA-circRNA interactions. In particular, we observed that circRNA12218 had binding sites for three miRNAs targeting ACAN, while circRNA12487 had binding sites for seven miRNAs targeting COL2A1. Our results add a novel set of genes and non-coding RNAs that could potentially serve as candidate diagnostic biomarkers or therapeutic targets for KBD patients.

Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function.

BACKGROUND: Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. METHODS: We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. RESULTS: We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (ß = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (ß = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (ß = 0.088, FDR = 0.010), whereas no such association was found with PRS (ß = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (ß = -0.042, FDR = 6.30 × 10-9), but not with PRS (ß = -0.003, FDR = 0.857). CONCLUSIONS: Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

Mendelian randomization study of the relationship between blood and urine biomarkers and schizophrenia in the UK Biobank cohort.

BACKGROUND: The identification of suitable biomarkers is of crucial clinical importance for the early diagnosis of treatment-resistant schizophrenia (TRS). This study aims to comprehensively analyze the association between TRS and blood and urine biomarkers. METHODS: Candidate TRS-related single nucleotide polymorphisms (SNPs) were obtained from a recent genome-wide association study. The UK Biobank cohort, comprising 376,807 subjects with blood and urine biomarker testing data, was used to calculate the polygenic risk score (PRS) for TRS. Pearson correlation analyses were performed to evaluate the correlation between TRS PRS and each of the biomarkers, using calculated TRS PRS as the instrumental variables. Bidirectional two-sample Mendelian randomization (MR) was used to assess potential causal associations between candidate biomarkers with TRS. RESULTS: Here we identify a significant association between TRS PRS and phosphate (r = 0.007, P = 1.96 × 10-4). Sex subgroup analyses identify seven and three candidate biomarkers associated with TRS PRS in male and female participants, respectively. For example, total protein and phosphate for males, creatinine and phosphate for females. Bidirectional two-sample MR analyses indicate that TRS is negatively associated with cholesterol (estimate = -0.363, P = 0.008). Conversely, TRS is positively associated with total protein (estimate = 0.137, P = 0.027), mean corpuscular volume (estimate = 0.032, P = 2.25 × 10-5), and mean corpuscular hemoglobin (estimate = 0.018, P = 0.007). CONCLUSIONS: Our findings provide insights into the roles of blood and urine biomarkers in the early detection and treatment of TRS.

People with schizophrenia experience periods of time during which they misperceive reality. Some people with schizophrenia do not respond well to the usual drugs that are used to relieve their symptoms. This type of schizophrenia is known as treatment-resistant schizophrenia (TRS). We looked at differences in the genes (inherited characteristics), blood and urine of a group of people in the UK with schizophrenia to see if people with TRS have particular characteristics that would enable them to be distinguished from patients with schizophrenia who tend to respond to usual treatment. We found several differences in the blood that could be used to predict which people might get TRS, including some that were specific to men or women. These discoveries are important because they can help doctors identify people who are more likely to develop TRS earlier, enabling them to avoid using treatments that might not work well for them.

Assessing the interaction effects of mitochondrial DNA polymorphisms and lifestyle on heel bone mineral density.

BACKGROUND: Bone mineral density (BMD) is a major predictor of osteoporotic fractures, and previous studies have reported the effects of mitochondrial dysfunction and lifestyle on BMD, respectively. However, their interaction effects on BMD are still unclear. Therefore, we aimed to investigate the possible interaction of mitochondrial DNA (mtDNA) and common lifestyles contributing to osteoporosis. METHODS: Our analysis included 119,120 white participants (Nfemale=65,949 and Nmale=53,171) from the UK Biobank with heel BMD phenotype data. A generalized linear regression model of PLINK was performed to assess the interaction effects of mtDNA and five life environmental factors on heel BMD, including smoking, drinking, physical activity, dietary diversity score, and vitamin D. In addition, we also performed linear regression analysis for total body BMD. Finally, we assessed the potential causal relationships between mtDNA copy number (mtDNA-CN) and life environmental factors using Mendelian randomization (MR) analysis. RESULTS: Our study identified four mtDNA loci showing suggestive evidence of heel BMD, such as m.16356T>C (MT-DLOOP; P =1.50×10-3) in total samples. Multiple candidate mtDNA×lifetsyle interactions were also detected for heel BMD, such as MT-ND2×physical activity (P = 2.88×10-3) in total samples and MT-ND1×smoking (P = 8.54×10-4) in males. Notably, MT-CYB was a common candidate mtDNA loci for heel BMD to interact with five life environmental factors. Multivariable MR analysis indicated a causal effect of physical activity on heel BMD when mtDNA-CN was considered (P =1.13×10-3). CONCLUSIONS: Our study suggests the candidate interaction between mitochondria and lifestyles on heel BMD, providing novel clues for exploring the pathogenesis of osteoporosis.

Long-term ambient air pollution and the risk of musculoskeletal diseases: A prospective cohort study.

Evidence of the associations of air pollution and musculoskeletal diseases is inconsistent. This study aimed to examine the associations between air pollutants and the risk of incident musculoskeletal diseases, such as degenerative joint diseases (n = 38,850) and inflammatory arthropathies (n = 20,108). An air pollution score was constructed to assess the combined effect of PM2.5, PM2.5-10, NO2, and NOX. Cox proportional hazard model was applied to assess the relationships between air pollutants and the incidence of each musculoskeletal disease. The air pollution scores exhibited the modest association with an increased risk of osteoporosis (HR = 1.006, 95% CI: 1.002-1.011). Among the individual air pollutants, PM2.5 and PM2.5-10 exhibited the most significant effect on elevated risk of musculoskeletal diseases, such as PM2.5 on osteoporosis (HR = 1.064, 95% CI: 1.020-1.110), PM2.5-10 on inflammatory arthropathies (HR = 1.059, 95% CI: 1.037-1.081). Females were found to have a higher risk of incident musculoskeletal diseases when exposed to air pollutants. Individuals with extreme BMI or lower socioeconomic status had a higher risk of developing musculoskeletal diseases. Our findings reveal that long-term exposure to ambient air pollutants may contribute to an increased risk of musculoskeletal diseases.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets.

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Assessing the association of coffee consumption on the relationship of chronic pain with depression and anxiety.

BACKGROUND: A bidirectional relationship between chronic pain (CP) and mental disorders has been reported, and coffee was believed to be associated with both. However, the association of coffee in this bidirectional relationship remains unclear. We aim to analyze the association of coffee consumption on the relationship of CP with depression and anxiety. METHODS: A total of 376,813 participants from UK Biobank were included. We collected data on anxiety, depression and CP from objects of our study population. The association of coffee consumption on the relationship of CP with depression and anxiety was assessed through logistic/linear regression models. Moreover, seemingly unrelated estimation test (SUEST) was used to compare whether the coefficients differed in two different groups. RESULTS: We observed significant associations of coffee consumption in the interaction of CP with depression and anxiety, such as the association of multisite chronic pain (MCP) on self-reported depression (ßcoffee = 0.421, ßnon-coffee = 0.488, PSUEST = 0.001), and the association of MCP on generalized anxiety disorder-7 (GAD-7) scores (ßcoffee = 0.561, ßnon-coffee = 0.678, PSUEST = 0.004) were significantly different between coffee drinking and non-coffee drinking groups. Furthermore, in analysis stratified by gender, we found headache (ßmale = 0.392, ßfemale = 0.214, PSUEST = 0.022) and hip pain (ßmale = 0.480, ßfemale = 0.191, PSUEST = 0.021) had significant associations with self-reported depression between males and females groups in coffee drinkers. CONCLUSIONS: Our results suggested that coffee consumption has a significant association on the relationship of CP with depression and anxiety.

Associations of classical HLA alleles with depression and anxiety.

Immune dysregulation has been widely observed in patients with psychiatric disorders. This study aims to examine the association between HLA alleles and depression and anxiety. Using data from the UK Biobank, we performed regression analyses to assess the association of 359 HLA alleles with depression and anxiety, as determined by Patient Health Questionnaire (PHQ) score (n = 120,033), self-reported depression (n = 121,685), general anxiety disorder (GAD-7) score (n = 120,590), and self-reported anxiety (n = 108,310). Subsequently, we conducted gene environmental interaction study (GEIS) to evaluate the potential effects of interactions between HLA alleles and environmental factors on the risk of depression and anxiety. Sex stratification was implemented in all analysis. Our study identified two significant HLA alleles associated with self-reported depression, including HLA-C*07:01 (ß = -0.015, p = 5.54 × 10-5 ) and HLA-B*08:01 (ß = -0.015, p = 7.78 × 10-5 ). Additionally, we identified four significant HLA alleles associated with anxiety score, such as HLA-DRB1*07:01 (ß = 0.084, p = 9.28 × 10-5 ) and HLA-B*57:01 (ß = 0.139, p = 1.22 × 10-4 ). GEIS revealed that certain HLA alleles interacted with environmental factors to influence mental health outcomes. For instance, HLA-A*02:07 × cigarette smoking was associated with depression score (ß = 0.976, p = 1.88 × 10-6 ). Moreover, sex stratification analysis revealed significant sex-based differences in the interaction effects of certain HLA alleles with environmental factors. Our findings indicate the considerable impact of HLA alleles on the risks of depression and anxiety, providing valuable insights into the functional relevance of immune dysfunction in these conditions.

A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank.

Bone mineral density (BMD) is an essential predictor of osteoporosis and fracture. We conducted a genome-wide trajectory analysis of BMD and analyzed the BMD change. PURPOSE: This study aimed to identify the genetic architecture and potential biomarkers of BMD. METHODS: Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform. RESULTS: A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10-126), FAM3C (P = 4.18 × 10-108), and CPED1 (P = 8.48 × 10-106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10-13) and RGS7 (P = 4.72 × 10-10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10-3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10-3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY. CONCLUSIONS: Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD.

A systematical association analysis of 25 common virus infection and genetic susceptibility of COVID-19 infection.

OBJECTIVES: Previous studies identified a number of diseases were associated with 2019 coronavirus disease (COVID-19). However, the associations between these diseases related viral infections and COVID-19 remains unknown now. METHODS: In this study, we utilized single nucleotide polymorphisms (SNPs) related to COVID-19 from genome-wide association study (GWAS) and individual-level genotype data from the UK biobank to calculate polygenic risk scores (PRS) of 487,409 subjects for eight COVID-19 clinical phenotypes. Then, multiple logistic regression models were established to assess the correlation between serological measurements (positive/negative) of 25 viruses and the PRS of eight COVID-19 clinical phenotypes. And we performed stratified analyses by age and gender. RESULTS: In whole population, we identified 12 viruses associated with the PRS of COVID-19 clinical phenotypes, such as VZV seropositivity for Varicella Zoster Virus (Unscreened/Exposed_Negative: ß = 0.1361, P = 0.0142; Hospitalized/Unscreened: ß = 0.1167, P = 0.0385) and MCV seropositivity for Merkel Cell Polyomavirus (Unscreened/Exposed_Negative: ß = -0.0614, P = 0.0478). After age stratification, we identified seven viruses associated with the PRS of eight COVID-19 clinical phenotypes in the age < 65 years group. After gender stratification, we identified five viruses associated with the PRS of eight COVID-19 clinical phenotypes in the women group. CONCLUSION: Our study findings suggest that the genetic susceptibility to different COVID-19 clinical phenotypes is associated with the infection status of various common viruses.

Association between electronic device use and health status among a middle-aged and elderly population: a cross-sectional analysis in the UK Biobank.

Aim: Few previous studies have investigated the impact of multiple types of electronic devices on health status, and the moderating effects of gender, age, and BMI. Our aim is to examine the relationships between the use of four types of electronics and three health status indicators in a middle-aged and elderly population, and how these relationships varied by gender, age, and BMI. Subject and methods: Using data from 376,806 participants aged 40-69 years in the UK Biobank, we conducted a multivariate linear regression to estimate the association between electronic device use and health status. Electronics use was categorized as TV watching, computer use, computer gaming, and mobile phone use, and health status included self-rated health (SRH), multisite chronic pain (MCP), and total physical activity (TPA). Interaction terms were utilized to assess whether the above associations were modified by BMI, gender, and age. Further stratified analysis was performed to explore the role of gender, age, and BMI. Results: Higher levels of TV watching (BSRH = 0.056, BMCP = 0.044, BTPA= -1.795), computer use (BSRH = 0.007, BTPA= -3.469), and computer gaming (BSRH = 0.055, BMCP = 0.058, BTPA= -6.076) were consistently associated with poorer health status (all P < 0.05). Contrastingly, earlier exposure to mobile phones (BSRH = -0.048, BTPA= 0.933, BMCP = 0.056) was inconsistent with health (all P < 0.05). Additionally, BMI (Bcomputer use-SRH= 0.0026, Bphone-SRH= 0.0049, BTV-MCP= 0.0031, and BTV-TPA= -0.0584) exacerbated the negative effects of electronics use, and male (Bphone-SRH = -0.0414, Bphone-MCP = -0.0537, Bphone-TPA= 2.8873) were healthier with earlier exposure to mobile phones (all P < 0.05). Conclusion: Our findings suggest that the adverse health effects associated with watching TV, computer use, and computer gaming were consistent and were moderated by BMI, gender, and age, which advances a comprehensive understanding of the association between multiple types of electronic devices and health status, and provides new perspectives for future research. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01886-5.

Combined effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression: A cross-sectional study based on UK Biobank.

Infection-induced perturbation of immune homeostasis could promote psychopathology. Psychiatric sequelae have been observed after previous coronavirus outbreaks. However, limited studies were conducted to explore the potential interaction effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression. In this study, first, polygenic risk scores (PRS) were calculated for eight COVID-19 clinical phenotypes using individual-level genotype data from the UK Biobank. Then, linear regression models were developed to assess the effects of COVID-19 PRS, C-reactive protein (CRP), systemic immune inflammation index (SII), and their interaction effects on the Generalized Anxiety Disorder-7 (GAD-7, 104 783 individuals) score and the Patient Health Questionnaire-9 (PHQ-9, 104 346 individuals) score. Several suggestive interactions between inflammation factors and COVID-19 clinical phenotypes were detected for PHQ-9 score, such as CRP/SII × Hospitalized/Not_Hospitalized in women group and CRP × Hospitalized/Unscreened in age >65 years group. For GAD-7 score, we also found several suggestive interactions, such as CRP × Positive/Unscreened in the age ≤65 years group. Our results suggest that not only COVID-19 and inflammation have important effects on anxiety and depression but also the interactions of COVID-19 and inflammation have serious risks for anxiety and depression.

Evaluating the role of anxiety on the association between irritable bowel syndrome and brain volumes: a mediation analysis in the UK Biobank cohort.

There is a strong link between irritable bowel syndrome and brain volumes, yet, to date, research examining the mediators of this association has been little. Based on the phenotypic data of 15 248 participants from the UK Biobank, a two-stage mediation analysis was performed to assess the association among brain volumes, anxiety, and irritable bowel syndrome. In the first stage, we identified the candidate mediating role of anxiety for irritable bowel syndrome associated with brain volumes using regression models. Then, we quantified the magnitude of the mediation effects by evaluating the average causal-mediated effect and proportion of mediation through performing mediation analyses in the R package in the second stage. In the first stage, we identified the partly mediating role of anxiety in the association between irritable bowel syndrome and the volume of thalamus (P left = 1.16 × 10-4, P right = 2.41 × 10-4), and grey matter (P left = 3.22 × 10-2, P right = 1.18 × 10-2) in the VIIIa cerebellum. In the second stage, we observed that the proportion of the total effect of irritable bowel syndrome on volume of thalamus mediated by anxiety was 14.3% for the left region (ß Average causal-mediated effect = -0.008, P Average causal-mediated effect = 0.004) and 14.6% for the right region (ß Average causal-mediated effect = -0.007, P Average causal-mediated effect = 0.006). Anxiety mediated 30.8% for the left region (ß Average causal-mediated effect = -0.013, P Average causal-mediated effect = 0.002) and 21.6% for the right region (ß Average causal-mediated effect = -0.010, P Average causal-mediated effect x= 0.018) of the total effect of irritable bowel syndrome on the volume of grey matter in the VIIIa cerebellum. Our study revealed the indirect mediating role of anxiety in the association between irritable bowel syndrome and brain volumes, promoting our understanding of the functional mechanisms of irritable bowel syndrome and its related psychosocial factors.

Identification of novel functional brain proteins for treatment-resistant schizophrenia: Based on a proteome-wide association study.

OBJECTIVE: Genetic approaches are increasingly advantageous in characterizing treatment-resistant schizophrenia (TRS). We aimed to identify TRS-associated functional brain proteins, providing a potential pathway for improving psychiatric classification and developing better-tailored therapeutic targets. METHODS: TRS-related proteome-wide association studies (PWAS) were conducted on genome-wide association studies (GWAS) from CLOZUK and the Psychiatric Genomics Consortium (PGC), which provided TRS individuals (n = 10,501) and non-TRS individuals (n = 20,325), respectively. The reference datasets for the human brain proteome were obtained from ROS/MAP and Banner, with 8,356 and 11,518 proteins collected, respectively. We then performed colocalization analysis and functional enrichment analysis to further explore the biological functions of the proteins identified by PWAS. RESULTS: In PWAS, two statistically significant proteins were identified using the ROS/MAP and then replicated using the Banner reference dataset, including CPT2 (PPWAS-ROS/MAP = 4.15 × 10-2 and PPWAS-Banner = 3.38 × 10-3) and APOL2 (PPWAS-ROS/MAP = 4.49 × 10-3 and PPWAS-Banner = 8.26 × 10-3). Colocalization analysis identified three variants that were causally related to protein expression in the human brain, including CCDC91 (PP4 = 0.981), PRDX1 (PP4 = 0.894), and WARS2 (PP4 = 0.757). We extended PWAS results from gene-based analysis to pathway-based analysis, identifying 14 gene ontology (GO) terms and the only candidate pathway for TRS, metabolic pathways (all P < 0.05). CONCLUSIONS: Our results identified two protein biomarkers, and cautiously support that the pathological mechanism of TRS is linked to lipid oxidation and inflammation, where mitochondria-related functions may play a role.

Assessing the effect of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes.

Longitudinal changes in brain structure and lifestyle can affect sleep phenotypes. However, the influence of the interaction between longitudinal changes in brain structure and lifestyle on sleep phenotypes remains unclear. Genome-wide association study dataset of longitudinal changes in brain structure was obtained from published study. Phenotypic data of lifestyles and sleep phenotypes were obtained from UK Biobank cohort. Using genotype data from UK Biobank, we calculated polygenetic risk scores of longitudinal changes in brain structure phenotypes. Linear/logistic regression analysis was conducted to evaluate interactions between longitudinal changes in brain structure and lifestyles on sleep duration, chronotype, insomnia, snoring and daytime dozing. Multiple lifestyle × longitudinal changes in brain structure interactions were detected for 5 sleep phenotypes, such as physical activity×caudate_age2 for daytime dozing (OR = 1.0389, P = 8.84 × 10-3) in total samples, coffee intake×cerebellar white matter volume_age2 for daytime dozing (OR = 0.9652, P = 1.13 × 10-4) in females. Besides, we found 4 overlapping interactions in different sleep phenotypes. We conducted sex stratification analysis and identified one overlapping interaction between female and male. Our results support the moderate effects of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes, and deepen our understanding of the pathogenesis of sleep disorders.

Treatment Resistance in Schizophrenia Is Associated with Attention Deficit/Hyperactivity Disorder and Gut Microbiota: A Genetic Correlation and Mendelian Randomization Study.

INTRODUCTION: Observational studies highlight associations of common diseases with individual schizophrenia symptoms. However, it is unclear whether these diseases are associated with individual treatment-resistant schizophrenia (TRS). We aimed to explore the genetic associations between common immune diseases, metabolic diseases, psychiatric disorders, gut microbiota and TRS. METHODS: Genome-wide association study (GWAS) summary data of European participants (n = ∼456,327) included TRS, 11 psychiatric disorders, 23 immune and metabolic diseases, body mass index, height, and 211 gut microbiota. In this genetic correlation and two-sample Mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with common immune diseases, metabolic diseases, psychiatric disorders, and gut microbiota with TRS. RESULTS: LDSC revealed candidate associations between attention deficit/hyperactivity disorder (ADHD), schizophrenia, intestinal infectious diseases, obesity and TRS (genetic correlation range, 0.230-0.702; p < 0.05). Two-sample MR analyses suggested that ADHD was positively associated with TRS (estimate [SE] = 0.204 [0.073], p = 0.005), a finding that remained stable across statistical models. Besides, schizophrenia and genus Barnesiella levels were causally associated with TRS but not consistent across MR approaches. CONCLUSION: This study reports genetic correlations between ADHD, schizophrenia, intestinal infectious diseases, obesity and TRS. The study also found that genus Barnesiella was associated with TRS. These findings may have clinical implications, highlighting the possible strategy for TRS prevention.

Assessing the effect of interaction between gut microbiome and inflammatory bowel disease on the risks of depression.

Background: Gut microbiome and inflammatory bowel disease (IBD) are implicated in the development of depression, but the effect of their interactions on the risk of depression remains unclear. We aim to analyze the effect of interactions between gut microbiome and IBD on the risk of depression, and explore candidate genes involving the interactions. Methods: Using the individual genotype and depression traits data from the UK Biobank, we calculated the polygenetic risk scores (PRS) of 114 gut microbiome, ulcerative colitis (UC), Crohn's disease (CD), and total IBD (CD + UC) respectively. The effects of interactions between gut microbiome and IBD on depression were assessed through a linear regression model. Moreover, for observed significant interactions between gut microbiome PRS and IBD PRS, PLINK software was used to test pair-wise single nucleotide polymorphisms (SNPs) interaction of corresponding gut microbiome PRS and IBD PRS on depression. Results: We found 64 candidate interactions between gut microbiome and IBD on four phenotypes of depression, such as F_Lachnospiraceae (RNT) × (CD + UC) for patient health questionnaire-9 (PHQ-9) score (P = 1.48 × 10-3), F_Veillonellaceae (HB) × UC for self-reported depression (P = 2.83 × 10-3) and P_Firmicutes (RNT) × CD for age at first episode of depression (P = 8.50 × 10-3). We observed interactions of gut-microbiome-associated SNPs × IBD-associated SNPs, such as G_Alloprevotella (HB)-associated rs147650986 (GPM6A) × IBD-associated rs114471990 (QRICH1) (P = 2.26 × 10-4). Conclusion: Our results support the effects of interactions between gut microbiome and IBD on depression risk, and reported several novel candidate genes for depression.